Search Results for author:
Found 17 papers, 7 papers with code
Searching for Exotic Particles in High-Energy Physics with Deep Learning
Standard approaches have relied on `shallow' machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning
The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5$\sigma$ significance barrier without more data.
Learning Activation Functions to Improve Deep Neural Networks
Artificial neural networks typically have a fixed, non-linear activation function at each neuron.
Ranked #162 on
Image Classification
on CIFAR-10
A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation
The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms.
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists.
Parameterized Machine Learning for High-Energy Physics
We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Learning in the Machine: Random Backpropagation and the Deep Learning Channel
It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system.
Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass.
Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning
Antihydrogen is at the forefront of antimatter research at the CERN Antiproton Decelerator.
Learning in the Machine: the Symmetries of the Deep Learning Channel
Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel.
Neural Network Regression with Beta, Dirichlet, and Dirichlet-Multinomial Outputs
We show that each target can be modeled as a sample from a Dirichlet distribution, where the parameters of the Dirichlet are provided by the output of a neural network, and that the combined model can be trained using the gradient of the data likelihood.
Sherpa: Robust Hyperparameter Optimization for Machine Learning
Sherpa is a hyperparameter optimization library for machine learning models.
Tourbillon: a Physically Plausible Neural Architecture
The circular autoencoders are trained in self-supervised mode by recirculation algorithms and the top layer in supervised mode by stochastic gradient descent, with the option of propagating error information through the entire stack using non-symmetric connections.
Quantitative Imaging Principles Improves Medical Image Learning
Differences between image types are primarily due to the imaging modality and medical images utilize a wide range of physics based techniques while natural images are captured using only visible light.
Diffusion Models for High-Resolution Solar Forecasts
Forecasting future weather and climate is inherently difficult.
FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation
These models are trained on a dataset of 50, 000 hand-annotated images containing 163 different fish species, ranging in length from 10cm to 250cm.
